Artificial cognitive control system based on the shared circuits model of sociocognitive capacities. A first approach

sharedcircuitmodels is presented in this work. The sharedcircuitsmodelapproach of sociocognitivecapacities recently proposed by Hurley in The sharedcircuitsmodel (SCM): how control, mirroring, and simulation can enable imitation, deliberation, and mindreading. Behavioral and Brain Sciences 31(1) (2008) 1–22 is enriched and improved in this work. A five-layer computational architecture for designing artificialcognitivecontrolsystems is proposed on the basis of a modified sharedcircuitsmodel for emulating sociocognitive experiences such as imitation, deliberation, and mindreading. In order to show the enormous potential of this approach, a simplified implementation is applied to a case study. An artificialcognitivecontrolsystem is applied for controlling force in a manufacturing process that demonstrates the suitability of the suggested approac

Artificial cognitive control with self-x capabilities: A case study of a micro-manufacturing process

Nowadays, even though cognitive control architectures form an important area of research, there are many constraints on the broad application of cognitive control at an industrial level and very few systematic approaches truly inspired by biological processes, from the perspective of control engineering. Thus, our main purpose here is the emulation of human socio-cognitive skills, so as to approach control engineering problems in an effective way at an industrial level. The artificial cognitive control architecture that we propose, based on the shared circuits model of socio-cognitive skills, seeks to overcome limitations from the perspectives of computer science, neuroscience and systems engineering. The design and implementation of artificial cognitive control architecture is focused on four key areas: (i) self-optimization and self-leaning capabilities by estimation of distribution and reinforcement-learning mechanisms; (ii) portability and scalability based on low-cost computing platforms; (iii) connectivity based on middleware; and (iv) model-driven approaches. The results of simulation and real-time application to force control of micro-manufacturing processes are presented as a proof of concept. The proof of concept of force control yields good transient responses, short settling times and acceptable steady-state error. The artificial cognitive control architecture built into a low-cost computing platform demonstrates the suitability of its implementation in an industrial setup

Grounding knowledge and normative valuation in agent-based action and scientific commitment

Philosophical investigation in synthetic biology has focused on the knowledge-seeking questions pursued, the kind of engineering techniques used, and on the ethical impact of the products produced. However, little work has been done to investigate the processes by which these epistemological, metaphysical, and ethical forms of inquiry arise in the course of synthetic biology research. An attempt at this work relying on a particular area of synthetic biology will be the aim of this chapter. I focus on the reengineering of metabolic pathways through the manipulation and construction of small DNA-based devices and systems synthetic biology. Rather than focusing on the engineered products or ethical principles that result, I will investigate the processes by which these arise. As such, the attention will be directed to the activities of practitioners, their manipulation of tools, and the use they make of techniques to construct new metabolic devices. Using a science-in-practice approach, I investigate problems at the intersection of science, philosophy of science, and sociology of science. I consider how practitioners within this area of synthetic biology reconfigure biological understanding and ethical categories through active modelling and manipulation of known functional parts, biological pathways for use in the design of microbial machines to solve problems in medicine, technology, and the environment. We might describe this kind of problem-solving as relying on what Helen Longino referred to as “social cognition” or the type of scientific work done within what Hasok Chang calls “systems of practice”. My aim in this chapter will be to investigate the relationship that holds between systems of practice within metabolic engineering research and social cognition. I will attempt to show how knowledge and normative valuation are generated from this particular network of practitioners. In doing so, I suggest that the social nature of scientific inquiry is ineliminable to both knowledge acquisition and ethical evaluations

Shared information structure: Evidence from cross-linguistic priming

This study asked whether bilinguals construct a language-independent level of information structure for the sentences that they produce. It reports an experiment in which a Polish–English bilingual and a confederate of the experimenter took turns to describe pictures to each other and to find those pictures in an array. The confederate produced a Polish active, passive, or conjoined noun phrase, or an active sentence with object–verb–subject order (OVS sentence). The participant responded in English, and tended to produce a passive sentence more often after a passive or an OVS sentence than after a conjoined noun phrase or active sentence. Passives and OVS sentences are syntactically unrelated but share information structure, in that both assign emphasis to the patient. We therefore argued that bilinguals construct a language-independent level of information structure during speech

Arquitectura de Control Cognitivo Artificial usando una plataforma computacional de bajo coste.

Hoy en día, las principales líneas de investigación tanto en Europa como de EEUU a nivel industrial, abordan aspectos como la interacción hombre-robot y dotar de inteligencia a las máquinas, y por tanto tienen un papel fundamental a la hora de desarrollar cualquier propuesta. Una manera de dotar a las máquinas de conocimiento de la operación que realizan y su interacción con el resto del flujo productivo es la utilización de arquitecturas de control inteligente artificial. A pesar que dichas arquitecturas están dentro de las áreas de investigación priorizadas, aún existen muchas restricciones para su aplicación en la industria de manera general. En este trabajo se propone la emulación de las experiencias socio-cognitivas del ser humano para la toma de decisiones a escala industrial. Las técnicas basadas en Lógica Borrosa, la optimización heurística y las técnicas de auto-aprendizaje desempeñan cada día un papel más importante a la hora de crear los diferentes niveles o capas dentro del sistema. En este trabajo se implementa una arquitectura de control cognitiva artificial enfocada en cuatro aspectos fundamentales: capacidades de auto-aprendizaje y auto-optimización para la estimación; portabilidad y escalabilidad basada en plataformas computacionales de bajo coste; conectividad basada en middleware y enfoque basado en modelos para la estimación y predicción de estados. Finalmente se muestran algunos ensayos de validación en un proceso de microtaladrado que muestran una buena respuesta transitoria y un error de estado estacionario aceptable. Sin lugar a dudas, con la arquitectura de control cognitivo artificial propuesta se sientan las bases para su futura aplicación en una instalación industrial

Directional adposition use in English, Swedish and Finnish

Directional adpositions such as to the left of describe where a Figure is in relation to a Ground. English and Swedish directional adpositions refer to the location of a Figure in relation to a Ground, whether both are static or in motion. In contrast, the Finnish directional adpositions edellä (in front of) and jäljessä (behind) solely describe the location of a moving Figure in relation to a moving Ground (Nikanne, 2003). When using directional adpositions, a frame of reference must be assumed for interpreting the meaning of directional adpositions. For example, the meaning of to the left of in English can be based on a relative (speaker or listener based) reference frame or an intrinsic (object based) reference frame (Levinson, 1996). When a Figure and a Ground are both in motion, it is possible for a Figure to be described as being behind or in front of the Ground, even if neither have intrinsic features. As shown by Walker (in preparation), there are good reasons to assume that in the latter case a motion based reference frame is involved. This means that if Finnish speakers would use edellä (in front of) and jäljessä (behind) more frequently in situations where both the Figure and Ground are in motion, a difference in reference frame use between Finnish on one hand and English and Swedish on the other could be expected. We asked native English, Swedish and Finnish speakers’ to select adpositions from a language specific list to describe the location of a Figure relative to a Ground when both were shown to be moving on a computer screen. We were interested in any differences between Finnish, English and Swedish speakers. All languages showed a predominant use of directional spatial adpositions referring to the lexical concepts TO THE LEFT OF, TO THE RIGHT OF, ABOVE and BELOW. There were no differences between the languages in directional adpositions use or reference frame use, including reference frame use based on motion. We conclude that despite differences in the grammars of the languages involved, and potential differences in reference frame system use, the three languages investigated encode Figure location in relation to Ground location in a similar way when both are in motion. Levinson, S. C. (1996). Frames of reference and Molyneux’s question: Crosslingiuistic evidence. In P. Bloom, M.A. Peterson, L. Nadel & M.F. Garrett (Eds.) Language and Space (pp.109-170). Massachusetts: MIT Press. Nikanne, U. (2003). How Finnish postpositions see the axis system. In E. van der Zee & J. Slack (Eds.), Representing direction in language and space. Oxford, UK: Oxford University Press. Walker, C. (in preparation). Motion encoding in language, the use of spatial locatives in a motion context. Unpublished doctoral dissertation, University of Lincoln, Lincoln. United Kingdo

Diseño e implementación de estrategias self-x en una arquitectura de control cognitivo artificial (ISCOSI_87/1314)

Los procesos de microfabricación son complejos procesos físicos que están continuamente cambiando en un entorno dinámico. En este contexto, el principal objetivo es el desarrollo de tecnologías computacionales y algoritmos con el fin de permitir un comportamiento de los procesos de microfabricación más rápido, auto organizado y auto optimizado por medio de sistemas de control inteligente. En este trabajo diseñamos e implementamos una arquitectura cognitiva artificial para controlar los procesos de fabricación. Además, los aspectos del hardware se han tenido en cuenta con el fin de desplegar la arquitectura desarrollada en una plataforma hardware de bajo coste para reducir los gastos de los equipos. Este trabajo está basado en una metodología con seis pasos principales. Primero, hemos diseñado una arquitectura cognitiva artificial inspirada en la aproximación Modi ed Shared Circuits Model, usando UML. Después de esto, la arquitectura ha sido desarrollada en Java. Una vez que la arquitectura ha sido implementada la hemos instanciado con el fin de probarla a través de estudios de simulación y experimentos reales en una instalación industrial. Otros objetivos de este trabajo son la instanciación con un algoritmo offline de optimización basado en el método de entropía cruzada y un algoritmo online de aprendizaje basado en Q-learning. De este modo hemos construido una instaciación con capacidades de auto optimización y auto aprendizaje. Es importante destacar el uso de sistemas de inferencia borrosa y sistemas de inferencia borrosa neuronal adaptados (ANFIS) escritos en C/C++, invocados desde Java gracias a la tecnología SWIG. Además, hemos dotado a la instanciación de la capacidad de ejecutarse en un entorno distribuido basado en el middleware ZeroC Ice. En consecuencia, la instanciación se ejecutar a en dos Raspberry Pi, con la unidad cognitiva y la ejecutiva desplegadas en cada una de ellas. También hemos usado el servicio IceGrid para dotar a la instanciación de la habilidad de descubrir los equipos sin tener que introducir manualmente las direcciones IP. Desde nuestra experiencia, la principal contribución de este trabajo es, además del diseño y la implementación de la arquitectura cognitiva artificial, la evaluación del rendimiento usando estudios de simulación y pruebas en tiempo real en una instalación industrial de microfabricaci ón. El objetivo de estos experimentos es comprobar la idoneidad de las funciones implementadas en la arquitectura y demostrar su capacidad de control ejecutándose en una hardware de bajo coste. Con ando en los resultados experimentales hemos demostrado que el control cognitivo artificial produce buenos resultados y promete oportunidades para tratar con sistemas complejos incluso ejecutándose en máquinas de poca potencia. Pero más importante es que este trabajo nos ofrece una base y un marco de trabajo computacional que habilitar a nuevas funciones para mejorar la arquitectura cognitiva artificial que hemos desarrollado.Micromanufacturing processes are complex physical processes which are continuously changing in a dynamic environment. In this context the main objective is the development of computational technologies and algorithms in order to enable faster, self-organized, self-optimized behavior of micromanufacturing processes by means of intelligent control systems. In this work we design and implement an arti cial cognitive architecture for controlling manufacturing processes. Moreover, hardware aspects are also considered in order to deploy the developed architecture on low-cost platform hardware in order to reduce the cost of the hosts. This work is based on a methodology with six main steps. Firstly, we have designed an arti cial cognitive architecture, inspired in the Modi ed Shared Circuits Model approach, using UML. After that, the architecture is developed and implemented in Java. Once the architecture is developed, we have built an instantiation of the architecture in order to test it by simulation studies and actual experiments in an industrial setup. Other targets of this work are the instantiation with an o -line optimization algorithm based on cross-entropy method and an on-line learning algorithm based on Q-learning method. Thereby we have built an instantiation with self-optimization and self-learning capabilities. It is important to note that inference models are fuzzy inference systems and Adaptive Neural Fuzzy Inference Systems (ANFIS) models written in C/C++, invoked from Java by means of the SWIG technology. In addition, we have provide the instantiation with the feature of running in a distributed environment on the basis of ZeroC Ice middleware. Thus the instantiation will run in two Raspberry Pi, with the cognitive unit and the executive unit deployed in each low-cost computing platform. We have also used the IceGrid service to provide the instantiation with the ability of discovering host without having to enter manually their IP addresses. From the best of our knowledge, one of the main contributions of this work is not only the design and implementation of the arti cial cognitive control architecture but also to assess the performance using simulation studies and real time tests in an industrial setup of micromanufacturing plan. The goal of theses experiments is to check the suitability of the functions implemented in the architecture and to demonstrate its control capability running in a low-cost hardware. Relying on the experimental results we have demonstrated that the arti cial cognitive control yields good results and promising opportunities to deal with complex systems even running in a low power machine as the Raspberry Pi. But most important is that this work give us the basement and the computational framework to enable new functions in order to improve the developed arti cial cognitive architecture

Artificial cognitive architecture with self-learning and self-optimization capabilities. Case studies in micromachining processes

Tesis doctoral inédita leída en la Universidad Autónoma de Madrid, Escuela Politécnica Superior, Departamento de Ingeniería Informática. Fecha de lectura : 22-09-201

Essentials of a Theory of Language Cognition

Cognition is not just ‘in the head’; it extends well beyond the skull and the skin. Non‐Cartesian Cognitive Science views cognition as being embodied, environmentally embedded, enacted, encultured, and socially distributed. The Douglas Fir Group (2016) likewise recognizes languages as emergent, social, integrated phenomena. Language is the quintessence of distributed cognition. Language cognition is shared across naturally occurring, culturally constituted, communicative activities. Usage affects learning and it affects languages, too. These are essential components of a theory of language cognition. This article summarizes these developments within cognitive science before considering implications for language research and teaching, especially as these concern usage‐based language learning and cognition in second language and multilingual contexts. Here, I prioritize research involving corpus‐, computational‐, and psycho‐linguistics, and cognitive psychological, complex adaptive system, and network science investigations of learner–language interactions. But there are many other implications. Looking at languages through any one single lens does not do the phenomena justice. Taking the social turn does not entail restricting our research focus to the social. Nor does it obviate more traditional approaches to second language acquisition. Instead it calls for greater transdisciplinarity, diversity, and collaborative work.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/147861/1/modl12532_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/147861/2/modl12532.pd